Multidimensional data visualizations using R
 

SIMP59: Data Selection and Visualisation, 7.5 credits VT25

nils.holmberg@iko.lu.se

Canvas info

This lecture focuses on multivariate data visualizations, demonstrating how to explore complex relationships between three or more variables using ggplot2 and dplyr data grouping. Participants will learn techniques for visualizing amounts, distributions, proportions, and x–y relationships while incorporating additional variables through color, size, and shape aesthetics. We will also cover methods for handling uncertainty in visualizations and using subplots and facets to effectively compare multiple groups within a dataset, enabling clearer insights into patterns and interactions in the data.

We will also focus on how to create dynamic and user-friendly visual representations using R. We will explore the basics of interactive plotting, including tools like plotly and ggplotly, as well as building interactive web applications with Shiny for R. Participants will also learn how to visualize results using 3D plots. Additionally, we will cover techniques for working with geospatial data, creating interactive maps to represent spatial patterns. Finally, the session will demonstrate best practices for publishing visualizations online, ensuring accessibility and engagement in data-driven storytelling.

Course literature

Wickham, Çetinkaya-Rundel, and Grolemund (2023)

Wilke (2019)

Watt and Naidoo (2025)

Lecture overview

  • multivariate data visualizations
  • ggplot2 (dplyr data grouping)
  • 1.5.4 Three or more variables
  • 5.1 Amounts
  • 5.2 Distributions
  • 5.3 Proportions
  • 5.4 x–y relationships
  • 5.6 Uncertainty
  • subplots, facets
  • interactive data visualizations
  • plotly for r
  • interactive, shiny for r
  • pca analysis and 3d plots
  • 5.5 Geospatial data
  • publishing visualizations online

Course literature

Wickham, Çetinkaya-Rundel, and Grolemund (2023)

Wilke (2019)

Palmer Penguins

test

Quantitative methods

    1. Experiments and
      Threats to Validity
    1. Survey Research,
      Questionnaire
    1. Quantitative
      Content Analysis

Lectures and workshops

Data collection (nov 12)

    1. Concept Explication and Measurement
    1. Reliability and Validity
    1. Effective ­Measurement
    1. Sampling
    1. Content Analysis

Exam question 1

Data analysis (nov 26)

    1. Experiments and Threats to Validity
    1. Survey Research
    1. Descriptive Statistics
    1. Inferential Statistics
    1. Multivariate Statistics

Exam question 2

9. Experiments and Threats to Validity

  • Random Assignment (p. 225)
  • Between-Subjects Design (p. 227)
  • Within-Subjects Design (p. 228)
  • Treatment Groups (p. 233)
  • Stimulus (p. 233)
  • Control Group (p. 238)

Next steps

Workshop 2, dec 2

References

Watt, H., and T. Naidoo. 2025. “Data Wrangling Recipes in r.” https://bookdown.org/hcwatt99/Data_Wrangling_Recipes_in_R/#why-data-wrangling-recipes-in-r.
Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science. 2nd ed. "O’Reilly Media, Inc.". https://r4ds.hadley.nz/.
Wilke, Claus O. 2019. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O’Reilly Media. https://clauswilke.com/dataviz/.